Transcriptional reprogramming of infiltrating neutrophils drives lung pathology in severe COVID-19 despite low viral load.

Troubling disparities in COVID-19-associated mortality emerged early, with nearly 70% of deaths confined to Black/African American (AA) patients in some areas. However, targeted studies on this vulnerable population are scarce. Here, we applied multiomics single-cell analyses of immune profiles from matching airways and blood samples of Black/AA patients during acute SARS-CoV-2 infection. Transcriptional reprogramming of infiltrating IFITM2+/S100A12+ mature neutrophils, likely recruited via the IL-8/CXCR2 axis, leads to persistent and self-sustaining pulmonary neutrophilia with advanced features of acute respiratory distress syndrome (ARDS) despite low viral load in the airways. In addition, exacerbated neutrophil production of IL-8, IL-1ß, IL-6, and CCL3/4, along with elevated levels of neutrophil elastase and myeloperoxidase, were the hallmarks of transcriptionally active and pathogenic airway neutrophilia. Although our analysis was limited to Black/AA patients and was not designed as a comparative study across different ethnicities, we present an unprecedented in-depth analysis of the immunopathology that leads to acute respiratory distress syndrome in a well-defined patient population disproportionally affected by severe COVID-19.

Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.

When examining datasets of any dimensionality, researchers frequently aim to identify individual subsets (clusters) of objects within the dataset. The ubiquity of multidimensional data has motivated the replacement of user-guided clustering with fully automated clustering. The fully automated methods are designed to make clustering more accurate, standardized and faster. However, the adoption of these methods is still limited by the lack of intuitive visualization and cluster matching methods that would allow users to readily interpret fully automatically generated clusters. To address these issues, we developed a fully automated subset identification and characterization (SIC) pipeline providing robust cluster matching and data visualization tools for high-dimensional flow/mass cytometry (and other) data. This pipeline automatically (and intuitively) generates two-dimensional representations of high-dimensional datasets that are safe from the curse of dimensionality. This new approach allows more robust and reproducible data analysis,+ facilitating the development of new gold standard practices across laboratories and institutions.

Nuclear apoptotic volume decrease in individual cells: Confocal microscopy imaging and kinetic modeling.

The dynamics of nuclear morphology changes during apoptosis remains poorly investigated and understood. Using 3D time-lapse confocal microscopy we performed a study of early-stage apoptotic nuclear morphological changes induced by etoposide in single living HepG2 cells. These observations provide a definitive evidence that nuclear apoptotic volume decrease (AVD) is occurring simultaneously with peripheral chromatin condensation (so called "apoptotic ring"). In order to describe quantitatively the dynamics of nuclear morphological changes in the early stage of apoptosis we suggest a general molecular kinetic model, which fits well the obtained experimental data in our study. Results of this work may clarify molecular mechanisms of nuclear morphology changes during apoptosis.

QFMatch: multidimensional flow and mass cytometry samples alignment.

Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.

Earth Mover's Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations.

Changes in the frequencies of cell subsets that (co)express characteristic biomarkers, or levels of the biomarkers on the subsets, are widely used as indices of drug response, disease prognosis, stem cell reconstitution, etc. However, although the currently available computational "gating" tools accurately reveal subset frequencies and marker expression levels, they fail to enable statistically reliable judgements as to whether these frequencies and expression levels differ significantly between/among subject groups. Here we introduce flow cytometry data analysis pipeline which includes the Earth Mover's Distance (EMD) metric as solution to this problem. Well known as an informative quantitative measure of differences between distributions, we present three exemplary studies showing that EMD 1) reveals clinically-relevant shifts in two markers on blood basophils responding to an offending allergen; 2) shows that ablative tumor radiation induces significant changes in the murine colon cancer tumor microenvironment; and, 3) ranks immunological differences in mouse peritoneal cavity cells harvested from three genetically distinct mouse strains.

A nonfitting method using a spatial sine window transform for inhomogeneous effective-diffusion measurements by FRAP.

Determining averaged effective diffusion constants from experimental measurements of fluorescent proteins in an inhomogeneous medium in the presence of ligand-receptor interactions poses problems of analytical tractability. Here, we introduced a nonfitting method to evaluate the averaged effective diffusion coefficient of a region of interest (which may include a whole nucleus) by mathematical processing of the entire cellular two-dimensional spatial pattern of recovered fluorescence. Spatially and temporally resolved measurements of protein transport inside cells were obtained using the fluorescence recovery after photobleaching technique. Two-dimensional images of fluorescence patterns were collected by laser-scanning confocal microscopy. The method was demonstrated by applying it to an estimation of the mobility of green fluorescent protein-tagged heterochromatin protein 1 in the nuclei of living mouse embryonic fibroblasts. This approach does not require the mathematical solution of a corresponding system of diffusion-reaction equations that is typical of conventional fluorescence recovery after photobleaching data processing, and is most useful for investigating highly inhomogeneous areas, such as cell nuclei, which contain many protein foci and chromatin domains.